Graduate Project Proposal: Analyzing the Impacts of Blister Rust on Eastern White
Pine Populations in the Encampment Forest Association with Regards to Various
Environmental Correlates in Northeast Minnesota by Creating a GIS Hazard Map
Anthony V. Nixon
Department of Resource Analysis, Saint Mary’s University of Minnesota, Minneapolis,
MN 55404
Keywords: White Pine Blister Rust (Cronartium ribocola), Eastern White Pine (Pinus
strobus), GIS (geographic information systems), Disease Hazard Mapping, ArcGIS,
Forest Management
Abstract
The populations of eastern white pine (Pinus strobus) throughout North America has
drastically declined over the past century. Vigorous management practices are being
implemented to preserve the species to previous historical dominance. However,
numerous damaging agents that affect the health and reproduction of the species have
hampered most expectations of successful restoration. White pine blister rust
(Cronartium ribocola), a lethal fungal disease of white pine, is a primary concern in the
reestablishment of white pine populations to the region. Blister rust management is a
risky investment without proper knowledge of the environmental correlates leading to
higher incidence levels. This proposal details the necessary steps for developing an
accurate blister rust hazard map using geographic information systems (GIS) focused in
northeastern Minnesota. Hazard mapping can lead to more efficient management
practices by focusing on specific environmental factors that elude to areas of “higher
hazard”.
What is the research question?
The research question proposed for the
graduate project is: “Can a high
resolution blister rust hazard map
accurately portray incidence levels of the
blister rust disease to provide
information for successful and efficient
management practices on the white pine
populations in northeast Minnesota.”
Background and need for the study?
Eastern white pine, losing much of its
known historical dominance, remains a
valuable species for biotic diversity,
aesthetics, wildlife habitat, and forest
products (Ostry, Laflamme, and
Katovich, 2010). It provides food,
safety, and habitat for numerous wildlife
species such as songbirds, snowshoe
hares, white-tailed deer, cottontails,
black bears, and pocket gophers. Along
with its significant importance to
ecosystems, eastern white pine also has
profound economic value. It is a
valuable timber species used in
woodworking as well as for Christmas
trees, mast production, and some foods
and medicines for Native Americans
(Ostry et al.).
The frequency of eastern white
pine is drastically lower in today’s
forests than in pre-settlement forests;
eastern white pine was heavily logged in
the 1800’s causing poor regeneration
rates due to the lack of mature seed
sources. Logging almost eliminated the
mature white pine resource and created
conditions for the destructive fires which
killed much of the remaining seedlings
and saplings (Ostry et al.).
As logging became banned in an
attempt to restore the species, other
threats and damaging agents arose that
have slowed the regeneration process.
Such threats included wildlife herbivory
(such as deer and cottontail browsing),
vegetation competition, and the white
pine weevil (Pissodes strobi) (Ostry et
al.). The biggest threat, however, came
from the arrival of blister rust
(Cronartium ribocola). For over a
century, white pine blister rust, caused
by the fungus Cronartium ribocola has
linked white pines and Ribes into a
disease pathosystem of serious concern
(Geils, Hummer, and Hunt, 2010).
In many cases, a silvicultural
approach that does not consider these
important local threats usually results in
planting failure, decreased tree growth,
poor tree form, and excessive tree
mortality. Specific management
practices have taken place to alleviate
the destruction caused from these
damaging agents. However, it requires a
large investment of time and money to
properly eradicate threats of blister rust.
Therefore, hazard mapping is used for
more efficient methods of blister rust
management.
White, Brown, and Host (2002)
created a hazard map for blister rust in
the Mixed Forest Province of Minnesota
using data obtained in 2002. However,
disease pathogens and various
environmental factors are continuously
changing over time, and technology
continues to advance enabling higher
resolution analyses that can lead to more
accurate results.
What is the value of this research?
The proposed research intends to
develop a high resolution white pine
blister rust hazard map for an area in
northeast Minnesota. The spatial
analyses of these hazard areas will show
any correlation of blister rust incidence
with local climate, topographic
characteristics (elevation, slope, and
aspect), vegetation, and distance from
water bodies and wetlands. By
understanding the environmental
correlates that affect the incidence levels
of blister rust, management practices can
be concentrated in localized regions and
not wasted in others.
What are the data needed for the
study?
A number of datasets are required to
create a high resolution hazard map
based on the various environmental
correlates that influence the incidence
level in blister rust. The primary dataset
required is information about white pine
blister rust occurrence. This dataset is a
cover type polygon that is delineated
from aerial photography. Polygons are
coded for blister rust presence that are at
least of light infestation.
High resolution topographic data
for measuring elevation for ground
surface and tree canopy is also required
for hazard mapping. This will consist of
Light Detection and Radar (LiDAR) data
that will be used to interpolate digital
elevation models (DEMs).
Temperature and climate data is
also essential for the creation of a hazard
map. This will be based on
climatological summaries derived over
specific periods of time. Spore dispersal
from Ribes occurs from July through
2
September, so only the climate variables
for these months will be used.
The distance from a water source
will be the final variable included in the
hazard map. Open water and wetland
data will be used to find the distances of
a water source to blister rust incidences.
which white pines occur, a field stating
that the polygon was surveyed in the
field, and the attributes for insect and
disease damage.
The topographic data will use
LiDAR classifications of 2 (bare earth)
and 5 (high vegetation). The attribute
required for analysis is the elevation
field.
The attributes essential for the
climate data will be the climate variables
specific to the months July, August, and
September. The climate variables used in
the hazard map will be mean minimum
temperature, mean maximum
temperature, total precipitation, and
potential evaporation (PET). The
necessary attribute for determining
distance to water source will be
calculated from provided water source
data.
What is the data collection procedure?
The data required for the project will
come from multiple online sources. The
white pine blister rust occurrence data is
provided by the Minnesota Department
of Natural Resources (MN DNR) Forest
Inventory Database. The dataset is stateowned and managed, and is updated
regularly.
The LiDAR data is provided by
Minnesota Geospatial Information
Office (MnGeo). It is a statewide dataset
that must be clipped to the specific
project site using custom tools provided
by the state organization.
The climate data is provided by
the National Climatic Data Center. The
climate data is published based on 30
year climatological summaries and
contain information on 12 climatic
variables.
Open water data is derived from
a Landsat-derived land-cover
classification provided by the Minnesota
DNR. The National Wetlands Inventory
(NWI) provided data for forested
wetland patches for the project area.
Describe how meaning will be made of
the raw data.
Raw data collected from the various
online sources mentioned will need to be
analyzed and manipulated to be useful
for creating a hazard map. All the data
sources will be clipped to the project
area for reducing distortion of outside
information. Blister rust occurrence data
is either field inventory or air
photograph polygons, so the specific
data for this project must be selected and
extracted based on the required attributes
mentioned for ease of use.
The LiDAR data will be used to
interpolate DEMs of the two
classifications of points for determining
the ground elevation and canopy cover
of the project site. Using the DEM from
ground elevation, the slope and aspect of
the site will also be determined and
utilized in the hazard map.
What data attributes are needed to
conduct a thorough analysis?
Specific data attributes are required
within each dataset for the premise of
this project. Attributes within the
polygons for determining the occurrence
of blister rust must have fields with
white pine present, cover types with
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Water source data will be
classified based on water body size
before calculating distances. A
Euclidean distance will then be
calculated to determine the distance
from a site to the nearest forested
wetland or water source for the project
region.
occurs in class j. A positive electivity
score indicates a likely association of
blister rust incidence with the
environmental variable interval, while
negative scores indicate a selection
against that variable for blister rust
incidence.
Once electivity values for each
variable are calculated, a map showing
the continuous distribution of blister rust
hazard by summing electivity scores for
all spatial variables is created. From the
map, three blister rust hazard classes can
be derived from statistical distribution of
electivity scores from the project site.
Based on decision risk assessment
protocols and standard deviations of
electivity scores, the classification of
white pine blister rust infection hazard is
as follows: low (blister rust may occur
sporadically but little management
intervention is required), indeterminate
(given no strong weight of evidence for
or against low or high hazard, it is
assumed blister rust can occur and
moderate management intervention may
be required), and high (highest
probability of blister rust infection
hazard, significant management
intervention is required to grow and
maintain eastern white pine).
What are the assigned analysis
methods?
Once all the preprocessing and
manipulating of the collected data is
performed, spatial data integration is the
first data analysis method to be
conducted. For all the different
environmental variables, the mean value
is calculated for all continuous data, and
the majority value is calculated for all
categorical data. This will capture the
central tendencies of the variables
throughout the project area. Integration
of the variables will be used to create a
predictive model for which any point on
the landscape is a function of climate,
elevation, topography, distance to water
source, and presence of Ribes.
An electivity index is then used
to test whether blister rust is randomly or
selectively distributed across the
landscape with respect to topography,
climate, and proximity to water. White,
Brown, and Host (2002) used this
formula for determining the electivity
index in a previous hazard model that
will be used for this project:
Provide examples of expected
deliverables to be generated from the
assigned analysis methods.
The expected deliverable generated for
the analysis methods will be a high
resolution hazard map based on the
determined blister rust classification.
Without performing the analysis, it is not
certain what the expected results are for
the project, however, based on previous
models it is expected that blister rust
incidence should be correlated with
(r )(1−p )
Eij = ln (p𝑖𝑗)(1−r 𝑗 )
𝑗
𝑖𝑗
where Eij is the electivity for blister rust
type i on spatial variable class j
(topography, climate, distance from
water). rij is the proportion of blister rust
class i on variable class j, and pj is the
proportion of the variable class that
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lower average temperatures, higher
moisture values, and steeper slopes.
a guideline for the methods used in this
project and is properly cited. Credible
and legal data sources must be used
throughout the collection phase of the
project. In addition, the project must be
completed on time and as efficiently as
possible.
Will this design and deliverables
answer the research question?
A successful construction of a high
resolution hazard map will accurately
provide information pivotal for high
efficiency management of the blister rust
disease within the project area.
Stakeholder considerations for the
project.
The key stakeholders for this project
include Saint Mary’s University of
Minnesota (SMUMN) and The Nature
Conservancy (TNC). SMUMN proposed
that students participate in a graduate
project of interest and is a major
influence on full completion of the
project for possible graduation. The
outcome of this project will be used by
TNC for management of blister rust
within the project area. TNC also
provides guidance when necessary for
successful creation of the hazard map.
The impacts of this project will provide
more efficient means of management of
blister rust which will reduce total costs
and time spent by TNC.
What specific problems might a
critical researcher anticipate with
regard to this study?
While working with regional averaged
data, a classification accuracy
assessment may show slight deviations
and errors when compared to actual site
surveyed blister rust incidence.
Therefore, with more funding and time,
the collection of specific, localized data
is required for most accurate hazard
mapping rather than relying exclusively
on regional averages.
To whom can the findings be
generalized?
References
The findings of the study can be
generalized towards any conservation
organizations that will perform blister
rust management within the project area.
It can also be used as a generalized
guideline for similar hazard mapping
production in other areas.
Geils, B. W., Hummer, K. E., and Hunt,
R. S. 2010. White Pines, Ribes, and
Blister Rust: A Review and
Synthesis
Ostry, M. E., Laflamme, G., and
Katovich, S. A. 2010. Silvicultural
Approaches for Management of
Eastern White Pine to Minimize
Impacts of Damaging Agents
White, M. A., Brown, T. N., and Host,
G. E. 2002. Landscape Analysis of
Risk Factors for White Pine Blister
Rust in the Mixed Forest Province of
Minnesota, U.S.A.
What specific ethical concerns exist
within this research?
Ethical concerns within the research
must take an unbiased approach towards
the questions proposed by the project. It
is important to note that the construction
of a previous hazard model was used as
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